scholarly journals Online Adaptation of Two-Parameter Inverter Model in Sensorless Motor Drives

2021 ◽  
Author(s):  
Jiahao Chen ◽  
Jie Mei ◽  
Xin Yuan ◽  
Yuefei Zuo ◽  
Jingwei Zhu ◽  
...  
Keyword(s):  
Current Model ◽  
Motor Drives ◽  
Voltage Variation ◽  
Current Parameter ◽  
Current Region ◽  
Effect Parameter ◽  
Bus Voltage ◽  
Two Parameters ◽  
Simultaneous Identification ◽  
Two Parameter

<div>This paper designs parameter adaptation algorithms for online simultaneous identification of a two-parameter sigmoid inverter model for compensating inverter nonlinearity to reduce the voltage error in flux estimation for a position sensorless motor drive. The inverter model has two parameters, a2 and a3, where a2 is “plateau voltage”, and a3 is a shape parameter that mainly accounts for the stray capacitor effect. Parameter a3 is identified by the (6k ± 1)-th order harmonics in measured current. Parameter a2 is identified by the amplitude mismatch of the estimated active flux. It is found that the classic linear flux estimator, i.e., the hybrid of voltage model and current model, cannot be used for a2 identification. This paper proposes to use a saturation function based nonlinear flux estimator to build an effective indicator for a2 error. The coupled identifiability of the two parameters is revealed and analyzed, which was not seen in literature. The concept of the low current region where the two-way coupling between a2 and a3 occurs is established. In theory, it is suggested to stop the inverter identification in the low current region. However, the experimental results in which dc bus voltage variation and load change are imposed, have shown the effectiveness of the proposed online inverter identification and compensation method, even in low current region.</div>

scholarly journals Online Adaptation of Two-Parameter Inverter Model in Sensorless Motor Drives

2022 ◽  
Author(s):  
Jiahao Chen ◽  
Jie Mei ◽  
Xin Yuan ◽  
Yuefei Zuo ◽  
Jingwei Zhu ◽  
...  
Keyword(s):  
Current Model ◽  
Motor Drives ◽  
Voltage Variation ◽  
Current Parameter ◽  
Current Region ◽  
Effect Parameter ◽  
Bus Voltage ◽  
Two Parameters ◽  
Simultaneous Identification ◽  
Two Parameter

<div>This paper designs parameter adaptation algorithms for online simultaneous identification of a two-parameter sigmoid inverter model for compensating inverter nonlinearity to reduce the voltage error in flux estimation for a position sensorless motor drive. The inverter model has two parameters, a2 and a3, where a2 is “plateau voltage”, and a3 is a shape parameter that mainly accounts for the stray capacitor effect. Parameter a3 is identified by the (6k ± 1)-th order harmonics in measured current. Parameter a2 is identified by the amplitude mismatch of the estimated active flux. It is found that the classic linear flux estimator, i.e., the hybrid of voltage model and current model, cannot be used for a2 identification. This paper proposes to use a saturation function based nonlinear flux estimator to build an effective indicator for a2 error. The coupled identifiability of the two parameters is revealed and analyzed, which was not seen in literature. The concept of the low current region where the two-way coupling between a2 and a3 occurs is established. In theory, it is suggested to stop the inverter identification in the low current region. However, the experimental results in which dc bus voltage variation and load change are imposed, have shown the effectiveness of the proposed online inverter identification and compensation method, even in low current region.</div>

scholarly journals Online Adaptation of Two-Parameter Inverter Model in Sensorless Motor Drives

2021 ◽  
Author(s):  
Jiahao Chen ◽  
Jie Mei ◽  
Xin Yuan ◽  
Yuefei Zuo ◽  
Jingwei Zhu ◽  
...  
Keyword(s):  
Current Model ◽  
Motor Drives ◽  
Voltage Variation ◽  
Current Parameter ◽  
Current Region ◽  
Effect Parameter ◽  
Bus Voltage ◽  
Two Parameters ◽  
Simultaneous Identification ◽  
Two Parameter

<div>This paper designs parameter adaptation algorithms for online simultaneous identification of a two-parameter sigmoid inverter model for compensating inverter nonlinearity to reduce the voltage error in flux estimation for a position sensorless motor drive. The inverter model has two parameters, a2 and a3, where a2 is “plateau voltage”, and a3 is a shape parameter that mainly accounts for the stray capacitor effect. Parameter a3 is identified by the (6k ± 1)-th order harmonics in measured current. Parameter a2 is identified by the amplitude mismatch of the estimated active flux. It is found that the classic linear flux estimator, i.e., the hybrid of voltage model and current model, cannot be used for a2 identification. This paper proposes to use a saturation function based nonlinear flux estimator to build an effective indicator for a2 error. The coupled identifiability of the two parameters is revealed and analyzed, which was not seen in literature. The concept of the low current region where the two-way coupling between a2 and a3 occurs is established. In theory, it is suggested to stop the inverter identification in the low current region. However, the experimental results in which dc bus voltage variation and load change are imposed, have shown the effectiveness of the proposed online inverter identification and compensation method, even in low current region.</div>

Two parameters are required for a Budyko function to describe the land-atmosphere interaction

2021 ◽  
Author(s):  
Daeha Kim ◽  
Jong Ahn Chun
Keyword(s):  
Land Surface ◽  
River Basins ◽  
Control Surface ◽  
Primary Control ◽  
Evaporative Demand ◽  
Atmosphere Interaction ◽  
The Mean ◽  
Two Parameters ◽  
Two Parameter ◽  
Surface Changes

&lt;p&gt;While the Budyko framework has been a simple and convenient tool to assess runoff (Q) responses to climatic and surface changes, it has been unclear how parameters of a Budyko function represent the vertical land-atmosphere interactions. Here, we explicitly derived a two-parameter equation by correcting a boundary condition of the Budyko hypothesis. The correction enabled for the Budyko function to reflect the evaporative demand (E&lt;sub&gt;p&lt;/sub&gt;) that actively responds to soil moisture deficiency. The derived two-parameter function suggests that four physical variables control surface runoff; namely, precipitation (P), potential evaporation (E&lt;sub&gt;p&lt;/sub&gt;), wet-environment evaporation (E&lt;sub&gt;w&lt;/sub&gt;), and the catchment properties (n). We linked the derived Budyko function to a definitive complementary evaporation principle, and assessed the relative elasticities of Q to climatic and land surface changes. Results showed that P is the primary control of runoff changes in most of river basins across the world, but its importance declined with climatological aridity. In arid river basins, the catchment properties play a major role in changing runoff, while changes in E&lt;sub&gt;p&lt;/sub&gt; and E&lt;sub&gt;w&lt;/sub&gt; seem to exert minor influences on Q changes. It was also found that the two-parameter Budyko function can capture unusual negative correlation between the mean annual Q and E&lt;sub&gt;p&lt;/sub&gt;. This work suggests that at least two parameters are required for a Budyko function to properly describe the vertical interactions between the land and the atmosphere.&lt;/p&gt;

scholarly journals Analysis of Model Predictive Current-Controlled Permanent Magnet Synchronous Motor Drives with Inaccurate DC Bus Voltage Measurement

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 353 ◽  
Author(s):  
Wei Wang ◽  
Zhixiang Lu
Keyword(s):  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Reference Value ◽  
Synchronous Motor ◽  
Motor Drives ◽  
Voltage Measurement ◽  
Variation Range ◽  
Torque Capacity ◽  
Dc Bus ◽  
Bus Voltage

In this paper, the effects of inaccurate DC bus voltage measurement are analyzed to model predictive current-controlled permanent magnet synchronous motor (PMSM) drives. It is found that the selection of the optimal space vector is affected by inaccurate DC bus voltage measurements, and the shortest distance principle is proposed to evaluate the effects. With the under-voltage measurement, the actual q axis current is always larger than the reference value, and PMSM may be damaged by the over-current phenomenon. However, the actual q axis current is always smaller than the reference value with the over-voltage measurement, and the rated torque capacity cannot be properly used. The effects of the over-voltage measurement are more serious than those of the under-voltage measurement. Additionally, the larger DC bus voltage measurement error can result in more serious effects than with the over-voltage measurement. Considering the limited variation range of the actual DC bus voltage, the rated value can be set as the measured value and the effects can be neglected. However, the effects should be taken into account if the variation range of the actual DC bus voltage is large. All the theoretical analyses are verified by experimental results.

On the computation of parameters that model snow avalanche motion

1981 ◽  
Vol 18 (1) ◽  
pp. 121-130 ◽  
Author(s):  
S. Bakkehøi ◽  
T. Cheng ◽  
U. Domaas ◽  
K. Lied ◽  
R. Perla ◽  
...  
Keyword(s):  
Snow Avalanche ◽  
Computational Problem ◽  
Avalanche Dynamics ◽  
Two Parameters ◽  
Two Parameter

This paper explores the computational problem of finding suitable numbers to use in a two-parameter model of snow avalanche dynamics. The two parameters are friction, μ, and a ratio of avalanche mass to drag, M/D. Given a path profile, and a maximum avalanche speed, then it is possible to compute unique values for u and M/D. If only the path profile and the stopping position are known, then it is possible to compute tables of pairs {μ, M/D} which can be tested as predictors of avalanche speeds. To generate these tables it is convenient to scale M/D in multiples of the total vertical drop of the path. The computations were tested on 136 avalanche paths. Values of {μ, M/D} were stratified, and certain values were rejected as unrealistic.

The Restricted Newton Method for Fast Nonlinear Model Predictive Control

2019 ◽  
Author(s):  
Anson Maitland ◽  
Chi Jin ◽  
John McPhee
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Newton’S Method ◽  
Newton's Method ◽  
Gradient Descent ◽  
Autonomous Vehicle ◽  
Optimization Method ◽  
Selection Strategies ◽  
Two Parameters ◽  
Two Parameter

Abstract We introduce the Restricted Newton’s Method (RNM), a basic optimization method, to accelerate model predictive control turnaround times. RNM is a hybrid of Newton’s method (NM) and gradient descent (GD) that can be used as a building block in nonlinear programming. The two parameters of RNM are the subspace on which we restrict the Newton steps and the maximal size of the GD step. We present a convergence analysis of RNM and demonstrate how these parameters can be selected for MPC applications using simple machine learning methods. This leads to two parameter selection strategies with different convergence behaviour. Lastly, we demonstrate the utility of RNM on a sample autonomous vehicle problem with promising results.

scholarly journals On a family of prior distributions for a class of Bayesian search models

1993 ◽  
Vol 25 (03) ◽  
pp. 714-716
Author(s):  
K. D. Glazebrook
Keyword(s):  
Conjugate Prior ◽  
Prior Distributions ◽  
Search Models ◽  
New Family ◽  
Special Cases ◽  
The Family ◽  
The One ◽  
Two Parameters ◽  
Two Parameter ◽  
Conjugate Prior Distributions

We propose a two-parameter family of conjugate prior distributions for the number of undiscovered objects in a class of Bayesian search models. The family contains the one-parameter Euler and Heine families as special cases. The two parameters may be interpreted respectively as an overall success rate and a rate of depletion of the source of objects. The new family gives enhanced flexibility in modelling.

scholarly journals DSP Implementation of a Neural Network Vector Controller for IPM Motor Drives

Energies ◽  
2019 ◽  
Vol 12 (13) ◽  
pp. 2558
Author(s):  
Yang Sun ◽  
Shuhui Li ◽  
Malek Ramezani ◽  
Bharat Balasubramanian ◽  
Bian Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Digital Signal Processor ◽  
Pulse Width Modulation ◽  
Digital Signal ◽  
Sampling Period ◽  
Motor Drives ◽  
Texas Instrument ◽  
High Efficient ◽  
Standard Vector ◽  
Bus Voltage

This paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP). The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions.

Lielmezs–Herrick correlation for the temperature-dependent surface tension of hydrocarbons

2016 ◽  
Vol 30 (23) ◽  
pp. 1650154 ◽  
Author(s):  
Cuihua Zhang ◽  
Huili Yi ◽  
Jianxiang Tian
Keyword(s):  
Surface Tension ◽  
The Other ◽  
Temperature Dependent ◽  
Two Parameters ◽  
Two Parameter

In this paper, we analyzed the ability of Lielmezs–Herrick (LH) correlation for the temperature-dependent surface tension of 28 hydrocarbons. We found that compared with other published correlations, the original LH correlation stands well only for four fluids. By using new data in REFPROP database, we refitted the two parameters of LH correlation. Two sets values are obtained. One is the updated corresponding state LH correlation, which is fluid independent. The other is the two-parameter LH correlation, which is fluid dependent. We found that the former clearly improves the accuracy of the original LH correlation and the latter is the best among all of the correlations we know.

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